用于企业应用的实验性规范挖掘

M. Schur
{"title":"用于企业应用的实验性规范挖掘","authors":"M. Schur","doi":"10.1145/2025113.2025169","DOIUrl":null,"url":null,"abstract":"Specification mining infers abstractions over a set of program execution traces. Whereas inductive approaches to specification mining rely on a given set of execution traces, experimental approaches systematically generate and execute test cases to infer rich models including uncommon and exceptional behavior. State-of-the-art experimental mining approaches infer low-level models representing the behavior of single classes. This paper proposes an approach for inferring models of built-in processes in enterprise systems based on systematic scenario test generation. The paper motivates the approach, sketches the relevant concepts and challenges, and discusses related work.","PeriodicalId":184518,"journal":{"name":"ESEC/FSE '11","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Experimental specification mining for enterprise applications\",\"authors\":\"M. Schur\",\"doi\":\"10.1145/2025113.2025169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Specification mining infers abstractions over a set of program execution traces. Whereas inductive approaches to specification mining rely on a given set of execution traces, experimental approaches systematically generate and execute test cases to infer rich models including uncommon and exceptional behavior. State-of-the-art experimental mining approaches infer low-level models representing the behavior of single classes. This paper proposes an approach for inferring models of built-in processes in enterprise systems based on systematic scenario test generation. The paper motivates the approach, sketches the relevant concepts and challenges, and discusses related work.\",\"PeriodicalId\":184518,\"journal\":{\"name\":\"ESEC/FSE '11\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ESEC/FSE '11\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2025113.2025169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ESEC/FSE '11","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2025113.2025169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

规范挖掘通过一组程序执行跟踪推断抽象。虽然规范挖掘的归纳方法依赖于一组给定的执行轨迹,但实验方法系统地生成并执行测试用例来推断包括不常见和异常行为的丰富模型。最先进的实验挖掘方法推断表示单个类行为的低级模型。提出了一种基于系统场景测试生成的企业系统内建过程模型推断方法。本文对该方法进行了激励,概述了相关概念和挑战,并对相关工作进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experimental specification mining for enterprise applications
Specification mining infers abstractions over a set of program execution traces. Whereas inductive approaches to specification mining rely on a given set of execution traces, experimental approaches systematically generate and execute test cases to infer rich models including uncommon and exceptional behavior. State-of-the-art experimental mining approaches infer low-level models representing the behavior of single classes. This paper proposes an approach for inferring models of built-in processes in enterprise systems based on systematic scenario test generation. The paper motivates the approach, sketches the relevant concepts and challenges, and discusses related work.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信